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Overview
We are seeking a Senior Staff Data Scientist to serve as the analytical leader and strategic thought partner for our Insurance business. This is a high-impact individual contributor role where you will set the analytics and measurement vision, raise the scientific bar, and influence the product, marketing, and business strategy for Insurance.
You will partner closely with senior leaders across Product, Marketing, Engineering, AI Science, and our insurance ecosystem to identify the biggest opportunities across the end-to-end member journey—from creating awareness and intent, to improving shopping and matching, to driving conversion and delivering measurable member value.
The ideal candidate combines deep analytical and scientific expertise with strong business judgment. You will operate effectively in ambiguity, connect insights across initiatives, and translate complex customer and marketplace dynamics into strategies that drive both member and business outcomes.
Responsibilities
- Set the analytics strategy for Insurance: Define the analytical and measurement vision across the end-to-end insurance journey, translating business strategy into a portfolio of high-impact data science opportunities.
- Influence business and product strategy: Serve as a strategic thought partner to senior Product, Marketing, and business leaders, using data, customer insights, and external trends to shape priorities and investment decisions.
- Build durable customer and marketplace understanding: Connect insights across behavioral data, experiments, models, and external signals to understand insurance needs, shopping behavior, price sensitivity, conversion, and member value.
- Lead experimentation and causal measurement: Establish rigorous approaches to measuring incrementality across the insurance funnel, including complex experiments and causal inference methodologies where traditional A/B testing is constrained.
- Advance personalization and matching: Partner with Product and AI Science to improve targeting, ranking, matching, and personalization—helping connect members with the right insurance opportunities at the right time.
- Shape the AI-native Insurance roadmap: Co-create the analytics and AI strategy for emerging insurance experiences, establishing measurement frameworks that connect model and experience quality to member and business outcomes.
- Define the measurement architecture: Establish the metrics, leading indicators, and frameworks used to evaluate the health of the Insurance business, including acquisition, engagement, conversion, retention, member savings/value, and economics.
- Identify and size new growth opportunities: Use first-principles analysis to uncover opportunities across the funnel, quantify their potential impact, and help leadership prioritize where to invest.
- Build scalable scientific capabilities: Develop reusable frameworks, methodologies, datasets, and analytical tools that improve decision-making across Insurance and can scale to the broader Personal Finance organization.
- Raise the scientific bar and develop talent: Mentor and elevate Data Scientists, establish analytical best practices, contribute to hiring and calibration, and scale your impact through others while remaining hands-on in the highest-leverage problems.
Qualifications
We’re looking for a strategic, curious, and influential data science leader who can combine scientific depth with strong business judgment.
- 9+ years of experience in data science and analytics, with a track record of driving strategy and measurable business impact across multiple initiatives or complex customer journeys.
- Demonstrated ability to apply first-principles thinking to translate ambiguous business problems into analytical strategies and actionable decisions.
- Deep expertise in experimentation, causal inference, customer segmentation, funnel optimization, and measurement, with the judgment to balance statistical rigor with business realities.
- Experience applying machine learning techniques—including classification, regression, ranking, targeting, or propensity modeling—to customer and business problems.
- Proven ability to connect insights across multiple analyses and initiatives to develop a broader understanding of customer behavior and influence product or business strategy.
- Experience creating reusable analytical frameworks, methodologies, and capabilities that are adopted beyond an individual project or team.
- Exceptional communication and stakeholder influence skills, with demonstrated ability to influence Director- and VP-level leaders across business and technical organizations.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Oakland $210,500 - $284,500
Salary Context
This $210K-$284K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Intuit, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills in Demand for This Role
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($247K) sits 30% above the category median. Disclosed range: $210K to $284K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Intuit AI Hiring
Intuit has 32 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, MLOps Engineer. Positions span Mountain View, CA, US, San Diego, CA, US, New York, NY. Compensation range: $116K - $369K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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